A method of identifying potential novel word usage in a document comprises determining a part-of-speech assignment for each word in the document using a first part-of-speech tagger, determining a part-of-speech assignment for each word in the document using a second part-of-speech tagger different from the first part-of-speech tagger, and comparing the part-of-speech assignment of the first and second part-of-speech taggers. The method then generates a differential word set having words with different part-of-speech assignment by the first and second part-of-speech taggers. The words in the differential word set are candidates of words of novel usage.
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10. A computer-readable article encoded with a computer-executable process, the process comprising:
assigning a first part-of-speech tag to words in at least one document according to a first part-of-speech tagging method;
assigning a second part-of-speech tag for words in the at least one document according to a second part-of-speech tagging method more simplistic than the first part-of-speech tagging method;
comparing the first and second part-of-speech tags;
generating a differential word set having words with different first and second part-of-speech tags; and
determining a weight to each word in the differential word set in response to the first part-of-speech tag of the word.
17. A system for identifying potential novel word usage in a document set comprising:
a microprocessor; and
a series of computer instructions comprising a method of:
assigning a first part-of-speech tag to words in at least one document according to a first part-of-speech tagging method;
assigning a second part-of-speech tag for words in at least one document according to a second part-of-speech tagging method more simplistic than the first part-of-speech tagging method;
comparing the first and second part-of-speech tags;
generating a differential word set having words with different first and second part-of-speech tags; and
selecting words of novel usage from the differential word set meeting a predetermined weight criteria.
1. A method of identifying potential novel word usage in a document, comprising:
determining a part-of-speech assignment for each word in the document using a first part-of-speech tagger;
determining a part-of-speech assignment for each word in the document using a second part-of-speech tagger different from the first part-of-speech tagger;
comparing the part-of-speech assignment of the first and second part-of-speech taggers;
generating a differential word set having words with different part-of-speech assignment by the first and second part-of-speech taggers, the words in the differential word set being candidates of words of novel usage; and
determining a weight to each word in the differential word set in response to the part-of-speech assignment of the word by the first part-of-speech tagger.
6. A method of identifying potential novel word usage in a document, comprising:
determining a part-of-speech assignment for each word in the document using a first part-of-speech tagger;
determining a part-of-speech assignment for each word in the document using a second part-of-speech tagger different from the first part-of-speech tagger;
comparing the part-of-speech assignment of the first and second part-of-speech taggers;
generating a differential word set having words with different part-of-speech assignment by the first and second part-of-speech taggers, the words in the differential word set being candidates of words of novel usage; and determining a weight to each word in the differential word set, wherein determining a weight to each word comprises determining a weight in response to a deviation from an expected part-of-speech usage of the word.
2. The method, as set forth in
3. The method, as set forth in
4. The method, as set forth in
W=Sd*WPOS(first POS tagger)*FR, where Sd is a difference sum that reflects the part-of-speech usage deviation from an expected part-of-speech usage of the word, WPOS(first POS tagger) is a weight based on the part-of-speech assignment for the word determined by the first part-of-speech tagger, and FR is a ratio of occurrence of the word in a document set comprising the document to a document corpus upon which the second part-of-speech tagger is based.
5. The method, as set forth in
7. The method, as set forth in
8. The method, as set forth in
9. The method, as set forth in
11. The article, as set forth in
12. The article, as set forth in
13. The article, as set forth in
14. The article, as set forth in
W=Sd*WPOS(first POS tagging method)*FR, where Sd is a difference sum that reflects the part-of-speech usage deviation from an expected part-of-speech usage of the word, WPOS(first POS tagging method) is a weight based on the first part-of-speech tag for the word, and FR is a ratio of occurrence of the word in a document set comprising the document to a document corpus upon which the second part-of-speech tagging method is based.
15. The article, as set forth in
16. The article, as set forth in
18. The system, as set forth in
19. The system, as set forth in
W=Sd*WPOS(first POS tagging method)*FR, where Sd is a difference sum that reflects the part-of-speech usage deviation from an expected part-of-speech usage of the word, WPOS(first POS tagging method) is a weight based on the first part-of-speech tag for the word, and FR is a ratio of occurrence of the word in a document set comprising the document to a document corpus upon which the second part-of-speech tagging method is based.
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The present invention relates generally to the field of computers and, in particular, to a system and method for identifying special word usage in a document.
The Information Highway built on the Internet and the World Wide Web has brought a tsunami of electronic data to everyone's computer. The large volumes of data make it difficult to adequately process, comprehend and utilize the content of the data. As one of the first steps commonly used to process documents, part-of-speech (POS) taggers have been used to tag or label text with the grammatical or syntactical parts of speech. Because a word may have different meaning depending on the context, POS tagging significantly enhances the understanding of the text. POS tagging also enables natural language processing tasks so that data may be summarized, categorized, and otherwise applied to some function in some form.
Language is dynamic, however, and words may acquire new meaning in/for certain segments of the population. For example, certain words or their usage may evolve in certain geographical regions or cultural/racial groups. As another example, certain groups of people, such as a scientific, technical, legal or another professional community, may coin new meaning for known words, or create new words and new word combinations. Therefore, it is desirable to recognize and identify such special or novel word usage so that better text understanding may be achieved.
In accordance with an embodiment of the present invention, a method of identifying potential novel word usage in a document comprises determining a part-of-speech assignment for each word in the document using a first part-of-speech tagger, determining a part-of-speech assignment for each word in the document using a second part-of-speech tagger different from the first part-of-speech tagger, and comparing the part-of-speech assignment of the first and second part-of-speech taggers. The method generates a differential word set having words with different part-of-speech assignment by the first and second part-of-speech taggers. The words in the differential word set are candidates of words of novel usage.
In accordance with another embodiment of the invention, a computer-readable article encoded with a computer-executable process comprises assigning a first part-of-speech tag to words in a plurality of documents according to a first part-of-speech tagging method, assigning a second part-of-speech tag for words in the plurality of documents according to a second part-of-speech tagging method more simplistic than the first part-of-speech tagging method, and comparing the first and second part-of-speech tags. The process further comprises generating a differential word set having words with different first and second part-of-speech tags.
In accordance with yet another embodiment of the present invention, a system for identifying novel word usage in a document set comprises a microprocessor, and a series of computer instructions comprising a method. The method comprises assigning a first part-of speech tag to words in a plurality of documents according to a first part-of-speech tagging method, assigning a second part-of-speech tag for words in the plurality of documents according to a second part-of-speech tagging method more simplistic than the first part-of-speech tagging method, comparing the first and second part-of-speech tags, and generating a differential word set having words with different first and second part-of-speech tags.
For a more complete understanding of the present invention, the objects and advantages thereof, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
The preferred embodiment of the present invention and its advantages are best understood by referring to
In blocks 14 and 16, two different part-of-speech (POS) taggers are used to analyze each document in the document set to generate a first and second tag set for each document. The first POS tagger is a tagger such as a transformational rule-based Brill POS tagger authored by Eric Brill, or one of its variations. To increase the accuracy of the first tagger, a combination of two or more thorough and accurate POS taggers may be used. The POS tag set used in the Brill POS tagger is the University of Pennsylvania Treebank POS tagset shown in TABLE A:
TABLE A
1
CC
Coordinating conjunction
2
CD
Cardinal number
3
DT
Determiner
4
EX
Existential “there”
5
FW
Foreign word
6
IN
Preposition or subordinating conjunction
7
JJ
Adjective
8
JJR
Adjective, comparative
9
JJS
Adjective, superlative
10
LS
List item marker
11
MD
Modal
12
NN
Noun, singular or mass
13
NNS
Noun, plural
14
NNP
Proper noun, singular
15
NNPS
Proper noun, plural
16
PDT
Predeterminer
17
POS
Possessive ending
18
PP
Personal pronoun
19
PPS
Possessive pronoun
20
RB
Adverb
21
RBR
Adverb, comparative
22
RBS
Adverb, superlative
23
RP
Particle
24
SYM
Symbol
25
TO
“to”
26
UH
Interjection
27
VB
Verb, base form
28
VBD
Verb, past tense
29
VBG
Verb, gerund or present participle
30
VBN
Verb, past participle
31
VBP
Verb, non-3rd person singular present
32
VBZ
Verb, 3rd person singular present
33
WDT
Wh-determiner
34
WP
Wh-pronoun
35
WPS
Possessive Wh-pronoun
36
WRB
Wh-adverb
The tag set shown in TABLE A is a very thorough set of grammatical tags that makes distinctions between different verb and noun usages, for example.
A simple or partial tagger, such as the second tagger used in block 16 may not distinguish between the various verb forms, for example. An example of a partial POS tagger is a corpus-based tagger, which is a database or corpus of collected written and/or spoken text that has already been grammatically tagged. An example of such statistical database is the Word Frequencies in Written and Spoken English: based on the British National Corpus by Leech, Geoffrey et al. (2001). The British National Corpus (BNC) is a 100,000,000 word electronic database sampled from present-day spoken and written English. Because the tag set used by the partial POS tagger is likely to be different than that used in the full-featured POS tagger, certain tags may need to be expanded. Alternatively, a corpus that uses the same tag set as the first POS tagger may be used for the second POS tagger.
In block 18, the tagged results from the full POS tagger (block 14) and the tagged results from the partial POS tagger (block 16) are compared to determine a differential word set that contains words that have been tagged differently by the two POS taggers. For example, a sentence, “Bob might race to win” may be tagged in this manner by the two POS taggers:
SENTENCE:
Bob
might
race
to
win
FIRST POS
NNP
MD
VB
IN
VB
TAGGER
SECOND POS
NNP
MD
NN
PREP
VB
TAGGER
NNP represents singular proper noun, MD represents modal, VB represents base form verb, IN represents preposition or subordinating conjunction, NN represents singular or mass noun, and PREP represents preposition. It may be seen that the word “race” is tagged differently by the two POS taggers. The first or full POS tagger has correctly tagged “race” as a verb, and the second or partial POS tagger has incorrectly tagged “race” as a noun. The word “race” will thus be included in the differential word set. Therefore, the process generates a differential word set or signature for each document in the document set of interest. A signature is an ordered vector highlighting the POS differences between the full tagger and the partial or corpus-based tagger. For example, the following may be a signature expressed in XML (extensible markup language) for a corpus in which new slang terms are being used:
<TaggerDifferences>
<Term>
<Spelling>swing</Spelling>
<Weight>34.44</Weight>
</Term>
...
<Term>
<Spelling>hoop</Spelling>
<Weight>3.67</Weight>
</Term>
</TaggerDifferences>
In block 20, a weighting is determined for each word in the differential word set of each document. In general, how a word is used in an entire document set is of interest. For example, if in a document set we find a particular word, “race,” is used as a verb 56.7% of the time and as a noun 43.3% of the time. These percentages are significantly different from the established 6.3% verb and 93.7% noun usage statistics. Referring also to block 38 in
Sd=ΣiεPOS tag set(|%(full POS tagger)−%(partial POS tagger)|)
Thus for the word, “race,” its difference sum would be:
Sd=|56.7−6.4|+|43.3−93.7|=100.8
In general, the value for the difference sum, Sd, will range from 0 to 200. Therefore, the difference sum reflects the present usage deviation from the established or expected POS usage of the word.
In block 40, a weighting based on the parts of speech of each word is determined. For example, words or terms that are nouns and verbs are typically of interest or more important than prepositions. As such, words used as nouns may receive a higher weighting than words used as prepositions. Therefore, the POS tagging by the full tagger is used as the basis to determine a POS-based weighting, WPOS(full tagger). There are various different ways to determine the relative weighting, such as modified steepest descent, principal component analysis, support vector machines, and other suitable approaches now known and later developed.
In block 42, a word frequency ratio is determined. The word frequency ratio, FR, is a number arrived at by combining a number of variables commonly used in the field of information retrieval, including term frequency, TF, inverse document frequency, IDF, and inverse (document) length, IL. TF measures the frequency by which a word appears in a document. IDF measures the relative occurrence of the word across many documents and is typically expressed as:
IDF=−log2 dfw/D,
Where dfw is document frequency or the number of documents that contain the word, and D is the number of documents in the document set. IL is (length of the document)−1. The weighting, W, can be a function of the above terms:
W=Sd*WPOS(full tagger)*TF*IL*IDF.
The expression, TF*IL*IDF, can be simplified to a variable called frequency ratio, FR, or the ratio of occurrence of the term in the document set of interest compared to the tagged corpus. Frequency ratio is a concept that is also commonly used in the field of information retrieval. Therefore, with the determination of FR in block 42, a weighting, W, for the word is determined in block 44, which can be expressed by:
W=Sd*WPOS(full tagger)*FR.
The process for determining a weight for each word in the differential word set is repeated for each document and ends in block 46.
Returning to
<WeightWordSet type=“DocumentSet” namespace=“Test”>
<Word>
<Spelling>race</Spelling>
<Weight>100.8</Weight>
<FullPOS>VB</FullPOS>
</Word>
<Word>
<Spelling>shingle</Spelling>
<Weight>134.5</Weight>
<FullPOS>VB</FullPOS>
</Word>
<Word>
<Spelling>chad</Spelling>
<Weight>144.5</Weight>
<FullPOS>VB</FullPOS>
</Word>
...
</WeightWordSet>
In the above example, three words or more have been identified in the differential word set of the document set. For each word, its weight and POS tag as determined by the full POS tagger are provided.
In block 24, a subset of the words in the differential word set of the document set is selected. The selected words are of high interest and are possibly slang, code words, jargon, words indicative of style, and other terms of interest. A number of criteria may be used alone or in combination to select the high interest words from the differential word set. For example, the selection criteria may include selecting a predetermined number of words with the highest weight, all words with weighting greater than or equal to a predetermined weight value, all words with greater than or equal to a predetermined percentage of the highest weighted words, and combinations of these and other suitable criteria. The result is a high interest word set for the document set.
In blocks 26-34, the resultant high interest word set is used in a number of exemplary applications described below to identify words used in a special manner so that documents containing these special word usages may be identified and/or classified, new trends for word usage may be identified and tracked, and better machine text understanding is possible.
In block 26, the high interest word set is used to identify documents in another document corpus that are similar in context to the document set. More specifically, the words in the high interest word set are used to cluster documents that may share similar characteristics as the document set. The “context” uncovered or indicated by the high interest word set may provide code words or words that are used in a novel manner in the document set. Because the high interest word set is derived from words that have been tagged differently by the POS taggers, the resultant words in the high interest word set are remarkably different than keywords derived by conventional or other keyword identification processes. In these processes, the keywords are typically used in their correct statistical POS distribution, not one that deviates from it. The conventional processes are especially ineffective where the documents are sequential (such as a series of electronic mail messages or follow-up messages or articles), and where the documents contain purposely obfuscated text. In these instances, the process described above and shown in
Slang is another type of word usage that may be detected by process 10, as shown in block 28. Slang is a word that is consistently used as a different parts of speech than its normal, conventional usage. The progressive adoption of slang may be identifiable and traceable across documents in a temporal order. In addition, unknown words can be represented separately from known words used in a novel way.
Jargon is another type of special word usage that may be detected by process 10, as shown in block 30. Jargon is special terminology used in a given field. Jargon is used more formally and typically distinguished from slang, which is used in informal language. Similar to slang, jargon can be a known word used in a different way from its statistical POS usage, or an unknown word.
Using process 10, the style and/or genre of documents, as characterized by novel word usage, may be detected, as shown in block 32. Therefore, these documents may be grouped accordingly to such determination. In particular, the absolute and relative use of words in a novel manner with respect to their POS statistics may be determined. For example, the mean value of the difference sum, Sd, across the entire document set may be determined. The mean value of Sd or μ(Sd) is high when the document set contains many novel uses of words. μ(Sd) can be weighted by word length, word novelty, and other statistics, and may be used to cluster the documents according to style and genre. Document clustering may be determined by a number of factors such as μ(Sd) and weighted μ(Sd), high interest word set, unknown words and their use, weighted high interest word set and/or weighted unknown words, and a weighted combination of one or more of the foregoing factors.
In block 34, nexus tracking refers to identifying trends in novel word usage across a corpus temporally, geographically and/or culturally. Such novel word usage trends may be indicative of document interrelationship and other associations, which may be further recognized and processed using other means such as keyword extraction, etc.
The previous applications shown in blocks 26-34 are examples provided that may benefit from the high interest word set generation process of the present invention. These high interest words may include such words as slang, code words, jargon, and words indicative of style and genre of the document. The manner in which this information may be used to improve text understanding is numerous and varied.
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